Developing Interdisciplinary Team Competencies in a Blended Learning Course: Impact on Student Learning
Bibliographic record
Abstract
Purpose: Health science educators increasingly focus on preparing health science students to work in interdisciplinary environments. Interdisciplinary communication is often hindered by geographic distances, creating barriers to effective interdisciplinary practice. Information and communication technologies are tools that can help reduce these barriers. Therefore, it is critical to ensure that students learn to communicate and collaborate with other disciplines in both face-to-face and on-line settings. The purpose of this article is to describe students’ perceptions of developing team skills in an interdisciplinary team context using a blended learning format. Understanding the students’ experiences will help health science educators prepare students effectively to use these technologies to facilitate interdisciplinary teamwork. Method: An interdisciplinary team development course was redesigned to be offered in a blended (70% on-line asynchronous/synchronous activities) format to increase flexibility and to provide experience with using the advanced communication technologies. This paper presents qualitative results obtained from student focus groups. The focus groups captured the students’ perspectives of the development of interdisciplinary team competencies in a blended learning format.Results: Although students generally felt they developed interdisciplinary team skills in a blended learning environment, they also expressed mixed feelings about how the environment affected the process of team development. Conclusions: Students’ perceptions of developing and practicing team skills in an interdisciplinary team context were not compromised in a blended learning format. Future research can further explore the on-line dynamics among students from various disciplines and the impact of this type of learning as a team on clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".